Macromolecular Imaging of Gray and White Matter Pathology in Multiple Sclerosis Multiple sclerosis (MS) is a complex inflammatory, demyelinating, neurodegenerative disease of the central nervous system, highly variable in its symptoms, clinical course, and underlying pathological changes in the brain. Conventional magnetic resonance imaging (cMRI) is essential to diagnosis and management of MS but it lacks sufficient sensitivity and specificity to MS pathology, correlates poorly with clinical disability and has limited prognostic value. In particular, cMRI fails to detect most of the disease burden in cortical gray matter (GM), recently recognized as a major site of pathology in MS, which is now understood to be a whole-brain (not exclusively white matter) disease. More sensitive, specific, and reliable imaging biomarkers are critically needed for earlier diagnosis and more accurate monitoring of disease progression and therapy. The overarching aim of this proposal is to meet this need through development of novel, clinically feasible, quantitative MRI methods based on the phenomenon of magnetization transfer (MT), a powerful yet underdeveloped method to quantify macromolecular changes in tissue. Despite a large volume of published work to date, MT imaging in its present form still lacks sufficient sensitivity, specificity, and reliability to meet the needs of MS management and clinical research. So-called quantitative MT imaging (qMTI) was developed to correct these deficiencies. The principal qMTI parameter, macromolecular pool fraction, has been shown to reflect myelin content and, when measured in cortical GM, predict clinical disability more accurately than any other imaging measure studied. These results could herald a paradigm shift in the assessment of MS as whole-brain disease; however, several obstacles still impede the application of qMTI in clinical and clinical research settings.
The first aim of this proposal will be to develop the next-generation qMTI methodology immune to cerebrospinal fluid partial volume effects and corrected for factors affecting tissue water content (edema/inflammation/gliosis), all optimized for imaging with a minimum number of MRI acquisitions.
The second aim will be to accelerate the constituent image acquisitions for this protocol, yielding a fast, robust, high- resolution implementation of qMTI for clinical settings. Finally, the third aim will be to validate the clinical utility of the new qMTI biomarkers in a cross-sectional cohort of MS patients and age-matched controls. Specifically, we will determine whether they predict cognitive impairment more accurately than existing imaging methods.

Public Health Relevance

) Project Narrative For NIH and other PHS agencies applications, this attachment will reflect the second component of the Project Summary. The second component of the Project Summary/Abstract (i.e., ?Description?) is Relevance. Using no more than two or three sentences, describe the relevance of this research to public health. In this section, be succinct and use plain language that can be understood by a general, lay audience. Multiple sclerosis (MS) is a complex degenerative disease of the central nervous system resulting in chronic, often debilitating symptoms. Magnetic resonance imaging (MRI) is an essential tool in the diagnosis and management of MS but it fails to capture the full extent of the disease or predict its course, which is highly variable. The overarching aim of this proposal is to develop new MRI technology capable of detecting disease invisible to current imaging methods; this would help to identify earlier those patients who will benefit from treatment, more accurately predict disease course and assess treatment results, and ultimately achieve a greater understanding of the disease and its multiple effects on the brain.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
1R01EB027087-01
Application #
9640653
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Liu, Guoying
Project Start
2018-09-30
Project End
2022-06-30
Budget Start
2018-09-30
Budget End
2019-06-30
Support Year
1
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of Wisconsin Madison
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
161202122
City
Madison
State
WI
Country
United States
Zip Code
53715